A well-crafted prompt is essential for obtaining accurate and relevant outputs from LLMs (Large Language Models). Prompt design enables users new to machine learning to control model output with minimal overhead. To facilitate rapid iteration and experimentation of LLMs at Uber, there was a need for centralization to seamlessly construct prompt templates, manage them, and execute them against various underlying LLMs to take advantage of LLM support tasks. To meet these needs, we built a prompt engineering toolkit that offers standard strategies that encourage prompt engineers to develop well-crafted prompt templates. It also provides functionality to enrich these templates with context obtained from RAG (Retrieval-Augmented Generation) and runtime feature datasets.

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Uber Engineering

A well-crafted prompt is vital for obtaining accurate outputs from Large Language Models (LLMs). To streamline this process, Uber developed a Prompt Engineering Toolkit that centralizes prompt template creation, management, and evaluation. The toolkit supports context enrichment, batch generation, version control, and safety measures to ensure responsible AI use. It includes a GenAI Playground for prompt exploration and advanced guidance techniques to enhance prompt quality. The toolkit is designed to facilitate LLM usage across development and production stages, offering a robust framework for effective prompt engineering.

Introducing the Prompt Engineering Toolkit